Wilbert Sanchez - Creative Assignment - 1

Loading libraries

Creating paths & Directory

Loading data required and creating categorical variables.

#vars_selected_household <- c("HINCP", "MRGX")
#vars_selected_person <- c("RAC1P", "SCHL", "MIL", "CIT")
#hampden_MA <- get_acs(geography = "county", variables = c("SCHL"), state = "MA", county = "Hampden", year = 2018, survey = "acs1")

#extracting variables in wide format
hampden_MA1 <- get_acs(geography = "tract", variables = c(medincome = "B19013_001", grad_degree = "B06009_005", total_pops = "B01003_001", housing_units_mortgage = "B25081_001", edu_attain_over_25yrs_bachelor = "B15003_022"), output = "wide",  state = "MA", county = "Hampden", year = 2018)
## Getting data from the 2014-2018 5-year ACS
hampden_MA1$perc_pops_grad <- "NA"
column_names <- c("perc_pops_grad")

hampden_MA1$grad_degreeE_numeric <- as.numeric(hampden_MA1$grad_degreeE)
hampden_MA1$total_popsE_numeric <- as.numeric(hampden_MA1$total_popsE)
hampden_MA1$total_HU_mortgage_numeric <- as.numeric(hampden_MA1$housing_units_mortgageE)
hampden_MA1$total_pops_numeric <- as.numeric(hampden_MA1$total_popsE)
hampden_MA1$medincome_numeric <- as.numeric(hampden_MA1$medincomeE)

#converting income into categorical variable
hampden_MA1$Income_categories <- cut(hampden_MA1$medincome_numeric, breaks=c(0,55000,73900,200000), labels = c("A","B","C"))



hampden_MA1$perc_pops_grad <- (hampden_MA1$grad_degreeE_numeric / hampden_MA1$total_popsE_numeric)   

#converting college graduate %s into categorical variables
summary(hampden_MA1$perc_pops_grad)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.06627 0.10285 0.10503 0.14484 0.23696
hampden_MA1$college_grad_perc <- cut(hampden_MA1$perc_pops_grad, breaks=c(0,.06,.15,.25), labels = c("A","B","C"))


#creating a final dataset
hampden_final <- hampden_MA1 %>% select(GEOID, NAME, grad_degreeE_numeric,total_popsE_numeric, total_HU_mortgage_numeric,total_pops_numeric, medincome_numeric,Income_categories, perc_pops_grad, college_grad_perc)

#summary(hampden_final)

Plotting data

#understanding the distribution of income
(histogram <- ggplot(hampden_final, aes(x=medincome_numeric)) + geom_histogram() + labs(x = "Median Income", y = "Count - Frequency", subtitle = "Distribution by Census Tract", title = "Hampden County: Median Annual Income"))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#income vs educational attainment
(scatter1 <- ggplot(data = hampden_final) + geom_point(mapping = aes(x =  perc_pops_grad, y = medincome_numeric)) + labs(x = "% of Total Population with a Graduate Degree", y = "Median Income", subtitle = "Distribution by Census Tract", title = "Hampden County: Pops with Graduate Degree vs\n Educational vs Median Income"))

#pops vs mortgage
(scatter2 <- ggplot(data = hampden_final) + geom_point(mapping = aes(x =  total_pops_numeric, y = total_HU_mortgage_numeric)) + labs(x = "Total Population", y = "Total Mortgages", subtitle = "Distribution by Census Tract", title = "Hampden County: Total Population vs \nTotal Mortgage Numbers") + facet_wrap( ~ Income_categories, nrow = 2))

#pops vs mortgage adjusted for educational attainment categories
(scatter3 <- ggplot(data = hampden_final) + geom_point(mapping = aes(x =  total_pops_numeric, y = total_HU_mortgage_numeric, color = college_grad_perc)) + labs(x = "Total Population", y = "Total Mortgages", subtitle = "Distribution by Census Tract", title = "Hampden County: Total Population vs Total Mortgage\nNumbers Adjusted for educational attainment categories"))

(scatter4 <- ggplot(data = hampden_final) + geom_point(mapping = aes(x =  total_pops_numeric, y = total_HU_mortgage_numeric, colour = medincome_numeric)) + labs(x = "Total Population", y = "Total Mortgages", subtitle = "Distribution by Census Tract", title = "Hampden County: Total Population vs \n Total Mortgage Numbers") + geom_smooth(aes(x =  total_pops_numeric, y = total_HU_mortgage_numeric))) 
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

#lollipop chart utilizing ggpubr
lollipop <- ggdotchart(hampden_final, x = "GEOID", y = "total_pops_numeric", color = "Income_categories", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
                        sorting = "ascending", add = "segments", ggtheme = theme_pubr()) + labs(x = "GEOID", y = "Total Population", title = "Hampden County: Ranked Population by Census Tract") + theme(axis.text=element_text(size=4))

#same graph as above with some x/y axis modifications
(lollipop2 <- lollipop + scale_y_continuous(limits=c(0, 8500))+theme(axis.text.x = element_text(angle=45)))

ggplotly(lollipop2, tooltip = "text") %>% style(hoveron = "fill")
#polar coordinates
polar_bar <- ggplot(hampden_final, aes(x=as.factor(GEOID), y= medincome_numeric)) + geom_bar(stat="identity", fill=alpha("blue", 0.3)) + ylim(-100,200000) +theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-2,4), "cm"))+ coord_polar(start = 0)

#polar_bar + labs(subtitle = "Distribution by Census Tract", title = "Hampden County: Median Annual Income Polar Bar Graph")

#preparing data to add labels
hampden_final$label_data <- hampden_final$medincome_numeric
(number_of_bar <- nrow(hampden_final))
## [1] 103
hampden_final$id <- seq(1,number_of_bar)
hampden_final$angle <-  90 - 360 * (hampden_final$id-0.5) /number_of_bar
hampden_final$hjust<-ifelse( hampden_final$angle < -90, 1, 0)
hampden_final$angle<-ifelse(hampden_final$angle < -90, hampden_final$angle +180, hampden_final$angle)


(polar_bar2 <- ggplot(hampden_final, aes(x=as.factor(GEOID), y=medincome_numeric)) + 
    geom_bar(stat="identity", fill=alpha("skyblue", 0.7)) + 
    ylim(-100,200000) +
    theme_minimal() +
    theme(axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm")) +
   coord_polar(start = 0) 
+geom_text(data=hampden_final, aes(x=GEOID, y=medincome_numeric+10, label=GEOID, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2, angle= hampden_final$angle, inherit.aes = FALSE ))

#heatmap
#constructing a numeric matrix
hampden_numeric <- hampden_final %>% select(GEOID, grad_degreeE_numeric,total_popsE_numeric, total_HU_mortgage_numeric, medincome_numeric)
heatmap(data.matrix(hampden_numeric, rownames.force = NA)) 

help(heatmap)